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jamiehudson
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725_model_v5

Text Classification
setfit
Safetensors
sentence-transformers
mpnet
generated_from_setfit_trainer
Eval Results
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725_model_v5 / README.md
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metadata
library_name: setfit
tags:
  - setfit
  - sentence-transformers
  - text-classification
  - generated_from_setfit_trainer
metrics:
  - accuracy
  - f1
  - precision
  - recall
widget:
  - text: >-
      man, product/whatever is my new best friend. i like product but the
      integration of product into office and product is a lot of fun. i just
      spent the day feeding it my training presentation i'm preparing in my day
      job and it was very helpful. almost better than humans.
  - text: >-
      that's great news! product is the perfect platform to share these advanced
      product prompts and help more users get the most out of it!
  - text: >-
      after only one week's trial of the new product with brand enabled, i have
      replaced my default browser product that i was using for more than 7 years
      with new product. i no longer need to spend a lot of time finding answers
      from a bunch of search results and web pages. it's amazing
  - text: >-
      very impressive. brand is finally fighting back. i am just a little
      worried about the scalability of such a high context window size, since
      even in their demos it took quite a while to process everything.
      regardless, i am very interested in seeing what types of capabilities a
      >1m token size window can unleash.
  - text: >-
      product the way it shows the sources is so fucking cool, this new ai is
      amazing
pipeline_tag: text-classification
inference: true
base_model: sentence-transformers/paraphrase-mpnet-base-v2
model-index:
  - name: SetFit with sentence-transformers/paraphrase-mpnet-base-v2
    results:
      - task:
          type: text-classification
          name: Text Classification
        dataset:
          name: Unknown
          type: unknown
          split: test
        metrics:
          - type: accuracy
            value: 0.964
            name: Accuracy
          - type: f1
            value:
              - 0.8837209302325582
              - 0.9130434782608696
              - 0.9781021897810218
            name: F1
          - type: precision
            value:
              - 1
              - 1
              - 0.9571428571428572
            name: Precision
          - type: recall
            value:
              - 0.7916666666666666
              - 0.84
              - 1
            name: Recall

SetFit with sentence-transformers/paraphrase-mpnet-base-v2

This is a SetFit model that can be used for Text Classification. This SetFit model uses sentence-transformers/paraphrase-mpnet-base-v2 as the Sentence Transformer embedding model. A LogisticRegression instance is used for classification.

The model has been trained using an efficient few-shot learning technique that involves:

  1. Fine-tuning a Sentence Transformer with contrastive learning.
  2. Training a classification head with features from the fine-tuned Sentence Transformer.

Model Details

Model Description

Model Sources

Model Labels

Label Examples
neither
  • 'i asked brand to write it and then let it translate back. so in reality i have no clue what i am sending...'
  • "i saw someone summarize brand the other day; it doesn't give answers, it gives answer-shaped responses."
  • 'thank you comrade i mean colleague. i will have brand summarize.'
peak
  • 'brand!! it helped me finish my resume. i just asked it if it could write my resume based on horribly written descriptions i came up with. and it made it all pretty:)'
  • 'been building products for a bit now and your product (audio pen) is simple, useful and just works (like the early magic when product came out). congratulations and keep the flag flying high. not surprised that india is producing apps like yours. high time:-)'
  • 'just got access to personalization in brand!! totally unexpected. very happy'
pit
  • 'brand recently i came across a very unwell patient in a psychiatric unit who was using product & this was reinforcing his delusional state & detrimentally impacting his mental health. anyone looking into this type of usage of product? what safe guards are being put in place?'
  • 'brand product is def better at extracting numbers from images, product failed (pro version) twice...'
  • "the stuff brand gives is entirely too scripted and impractical, which is what i'm trying to avoid:/"

Evaluation

Metrics

Label Accuracy F1 Precision Recall
all 0.964 [0.8837209302325582, 0.9130434782608696, 0.9781021897810218] [1.0, 1.0, 0.9571428571428572] [0.7916666666666666, 0.84, 1.0]

Uses

Direct Use for Inference

First install the SetFit library:

pip install setfit

Then you can load this model and run inference.

from setfit import SetFitModel

# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("jamiehudson/725_model_v5")
# Run inference
preds = model("product the way it shows the sources is so fucking cool, this new ai is amazing")

Training Details

Training Set Metrics

Training set Min Median Max
Word count 3 31.6606 98
Label Training Sample Count
pit 277
peak 265
neither 1105

Training Hyperparameters

  • batch_size: (32, 32)
  • num_epochs: (1, 1)
  • max_steps: -1
  • sampling_strategy: oversampling
  • body_learning_rate: (2e-05, 1e-05)
  • head_learning_rate: 0.01
  • loss: CosineSimilarityLoss
  • distance_metric: cosine_distance
  • margin: 0.25
  • end_to_end: False
  • use_amp: False
  • warmup_proportion: 0.1
  • seed: 42
  • eval_max_steps: -1
  • load_best_model_at_end: False

Training Results

Epoch Step Training Loss Validation Loss
0.0000 1 0.3157 -
0.0012 50 0.2756 -
0.0023 100 0.2613 -
0.0035 150 0.278 -
0.0047 200 0.2617 -
0.0058 250 0.214 -
0.0070 300 0.2192 -
0.0082 350 0.1914 -
0.0093 400 0.1246 -
0.0105 450 0.1343 -
0.0117 500 0.0937 -
0.0129 550 0.075 -
0.0140 600 0.0479 -
0.0152 650 0.0976 -
0.0164 700 0.0505 -
0.0175 750 0.0149 -
0.0187 800 0.0227 -
0.0199 850 0.0276 -
0.0210 900 0.0033 -
0.0222 950 0.0015 -
0.0234 1000 0.0008 -
0.0245 1050 0.0005 -
0.0257 1100 0.001 -
0.0269 1150 0.0009 -
0.0280 1200 0.0004 -
0.0292 1250 0.0007 -
0.0304 1300 0.001 -
0.0315 1350 0.0004 -
0.0327 1400 0.0005 -
0.0339 1450 0.0003 -
0.0350 1500 0.0004 -
0.0362 1550 0.0002 -
0.0374 1600 0.0004 -
0.0386 1650 0.0003 -
0.0397 1700 0.0003 -
0.0409 1750 0.0005 -
0.0421 1800 0.0004 -
0.0432 1850 0.0003 -
0.0444 1900 0.0002 -
0.0456 1950 0.0002 -
0.0467 2000 0.0003 -
0.0479 2050 0.0002 -
0.0491 2100 0.0001 -
0.0502 2150 0.0002 -
0.0514 2200 0.0256 -
0.0526 2250 0.0001 -
0.0537 2300 0.0124 -
0.0549 2350 0.0004 -
0.0561 2400 0.0125 -
0.0572 2450 0.0001 -
0.0584 2500 0.0002 -
0.0596 2550 0.0002 -
0.0607 2600 0.0001 -
0.0619 2650 0.0002 -
0.0631 2700 0.0002 -
0.0643 2750 0.0243 -
0.0654 2800 0.0001 -
0.0666 2850 0.0001 -
0.0678 2900 0.0001 -
0.0689 2950 0.0002 -
0.0701 3000 0.006 -
0.0713 3050 0.0021 -
0.0724 3100 0.0003 -
0.0736 3150 0.0003 -
0.0748 3200 0.0001 -
0.0759 3250 0.0 -
0.0771 3300 0.0002 -
0.0783 3350 0.0001 -
0.0794 3400 0.0 -
0.0806 3450 0.0124 -
0.0818 3500 0.0001 -
0.0829 3550 0.0001 -
0.0841 3600 0.0001 -
0.0853 3650 0.0 -
0.0864 3700 0.0042 -
0.0876 3750 0.0001 -
0.0888 3800 0.0004 -
0.0900 3850 0.0001 -
0.0911 3900 0.0 -
0.0923 3950 0.004 -
0.0935 4000 0.0002 -
0.0946 4050 0.0001 -
0.0958 4100 0.0001 -
0.0970 4150 0.0 -
0.0981 4200 0.0 -
0.0993 4250 0.0008 -
0.1005 4300 0.0 -
0.1016 4350 0.0 -
0.1028 4400 0.0 -
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0.1168 5000 0.0 -
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0.1192 5100 0.0 -
0.1203 5150 0.0008 -
0.1215 5200 0.001 -
0.1227 5250 0.0 -
0.1238 5300 0.0 -
0.1250 5350 0.0057 -
0.1262 5400 0.0014 -
0.1273 5450 0.0001 -
0.1285 5500 0.0001 -
0.1297 5550 0.0001 -
0.1308 5600 0.0001 -
0.1320 5650 0.0001 -
0.1332 5700 0.0 -
0.1343 5750 0.0 -
0.1355 5800 0.0004 -
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0.1378 5900 0.0001 -
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0.1963 8400 0.0127 -
0.1974 8450 0.0001 -
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0.1998 8550 0.0 -
0.2009 8600 0.0249 -
0.2021 8650 0.0003 -
0.2033 8700 0.0 -
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0.2804 12000 0.0 -
0.2815 12050 0.0 -
0.2827 12100 0.0 -
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0.2920 12500 0.0 -
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Framework Versions

  • Python: 3.10.12
  • SetFit: 1.0.3
  • Sentence Transformers: 2.5.1
  • Transformers: 4.38.2
  • PyTorch: 2.1.0+cu121
  • Datasets: 2.18.0
  • Tokenizers: 0.15.2

Citation

BibTeX 

@article{https://doi.org/10.48550/arxiv.2209.11055,
    doi = {10.48550/ARXIV.2209.11055},
    url = {https://arxiv.org/abs/2209.11055},
    author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
    keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
    title = {Efficient Few-Shot Learning Without Prompts},
    publisher = {arXiv},
    year = {2022},
    copyright = {Creative Commons Attribution 4.0 International}
}